/// <summary>
/// Register feature to the CLassifier
/// Format:G,N,%C,%R,%U,%D
/// </summary>
/// <param name="datastructureGroup"></param>
/// <param name="elementSize">Number of elements in the data structure at the time of an operation, N</param>
/// <param name="percentC">%C</param>
/// <param name="percentR">%R</param>
/// <param name="percentU">%U</param>
/// <param name="percentD">%D</param>
public void RegisterFeature(DataStructureGroup datastructureGroup, int elementSize, double percentC, double percentR, double percentU, double percentD, CrudBasedCollection.C5DataStructure currentDS)
{
while (!isBusy)//this is to make sure that the next feature will be registered only after the prior one has been completely registered.
{
isBusy = true;
currentDataStructure = currentDS;
//1. Encode and normalize the feature values
double[] x = new double[10]; //10 input values of the ANN
double[] encodedNormInterfaceAndSetBagProperty = EncodeNormalizeDataStructureGroup(datastructureGroup); //X1 Feature (5-length array)
Array.Copy(encodedNormInterfaceAndSetBagProperty, 0, x, 0, 5); //copy to the first 5 arrays
x[5] = (elementSize - gaussMeanX2) / gaussStdvX2; //X2 Feature
x[6] = (percentC - gaussMeanX3) / gaussStdvX3; //X3 Feature
x[7] = (percentR - gaussMeanX4) / gaussStdvX4; //X4 Feature
x[8] = (percentU - gaussMeanX5) / gaussStdvX5; //X5 Feature
x[9] = (percentD - gaussMeanX6) / gaussStdvX6; //X6 Feature;
//2. Register to Classifier and compute the data structure
double[] computed_y = Classifier.ComputeOutputs(x);//9 output values of the ANN
//using winner-take-all method to evaluate y values
int maxComputedYIndex = MaxIndex(computed_y);
string computedDS = yValues[maxComputedYIndex];
ngram.Dequeue();
ngram.Enqueue(computedDS);
//3. Register the computed DS as an N-Gram sequence to the Predictor
Predictor.RegisterSequence(ngram.ToArray());
string[] ngramMinus1 = new string[NValue - 1];
Array.Copy(ngram.ToArray(), 1, ngramMinus1, 0, NValue - 1);
CrudBasedCollection.C5DataStructure predictedDS = GetC5DataStructure(Predictor.PredictNext(ngramMinus1));
isBusy = false;
registerCount++;
//For debugging or simulation purposes only, comment this line to improve performance
OnFeatureRegistered(new FeatureRegisteredEventArgs(datastructureGroup, elementSize, percentC, percentR, percentU, percentD, computedDS, currentDataStructure.ToString(), Predictor.PredictedProbabilty, predictedDS.ToString(), registerCount));//raise an event
bool reset = false;
DecisionMaker.MakeDecision(registerCount, currentDataStructure, predictedDS, Predictor.PredictedProbabilty, out reset);
if (reset)
{
registerCount = 0;
}
break;
}
}
public void MakeDecision(int registeredSubsequenceCount, CrudBasedCollection.C5DataStructure currentDataStructure, CrudBasedCollection.C5DataStructure predictedDataStructure, double probability, out bool resetCount)
{
resetCount = false;
//main logic for the dicision making
if (registeredSubsequenceCount >= RegisteredDatastructureCountThreshold && // then check if data structure has been registered to the ngram for a certian threshold
probability >= PredictedPropbabilityThreshold) // and the predicted probability number is equal or greater than a certain threshold
{
//trace the number of Yes
YesDecisionCount++;
if (predictedDataStructure != CrudBasedCollection.C5DataStructure.Unknown && //if the predicted DS is not Unknown or not the same as the current one
currentDataStructure != predictedDataStructure)
{
PredictedDataStructure = predictedDataStructure;
resetCount = true; //the count will be reset every transformation is made
if (IsDynamicMode) //Make notify only when run on Dynamic Mode
{
OnTransformationNotified(new TransformNotifiedEventArgs(predictedDataStructure)); //Raise an event to notify the Green data structure to transform to the specific data structure
}
}
}
}
public TransformNotifiedEventArgs(CrudBasedCollection.C5DataStructure dataStructure)
{
this.TransformToDataStructure = dataStructure;
}
